Forward Kinematics for 2 DOF Planar Robot using Linear Genetic Programming

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@Article{DBLP:journals/rcs/ArellanoR19,

• author = "Humberto Velasco Arellano and Martin Montes Rivera",
• title = "Forward Kinematics for 2 {DOF} Planar Robot using Linear Genetic Programming",
• journal = "Research in Computing Science",
• volume = "148",
• number = "6",
• pages = "123--133",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, forward kinematics, automatic robot modeling, linear genetic programming",
• ISSN = "1870-4069",
• URL = "https://www.rcs.cic.ipn.mx/2019_148_6/Forward%20Kinematics%20for%202%20DOF%20Planar%20Robot%20using%20Linear%20Genetic%20Programming.pdf",
• timestamp = "Wed, 17 Feb 2021 00:00:00 +0100",
• biburl = "https://dblp.org/rec/journals/rcs/ArellanoR19.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "11 pages",
• abstract = "In the field of robotics, forward kinematics is an activity that allows finding a mathematical model for the resulting position in the final effector based on the robot joints position, a popular alternative for determining this model is defined by the Denavit Hartenberg convention, nevertheless, this method requires knowledge about linear algebra and three-dimensional spatial kinematics. Machine learning uses specific computational methodologies to solving similar problems in several areas, so it could be a viable answer for automatic determining of forwarding kinematics. we propose the use of genetic programming as a machine learning algorithm for finding the forward kinematics of a 2 degrees of freedom robot, getting a satisfactory outcome obtaining a satisfactory result with blocks that describe the expected solution, validating the capacity of the genetic programming in order to validate this algorithm for later work with more complex robots.",
• notes = "https://www.rcs.cic.ipn.mx/",

}

Genetic Programming entries for Humberto Velasco Arellano Martin Montes Rivera

Citations